气候变化领域集成服务门户(CCPortal): Using machine learning to synthesize spatiotemporal data for modelling DBH-height and DBH-height-age relationships in boreal forests

CCPortal

DOI	10.1016/j.foreco.2020.118104
	Using machine learning to synthesize spatiotemporal data for modelling DBH-height and DBH-height-age relationships in boreal forests
	Chen J.; Yang H.; Man R.; Wang W.; Sharma M.; Peng C.; Parton J.; Zhu H.; Deng Z.
发表日期	2020
ISSN	0378-1127
卷号	466
英文摘要	Sustainable forest management requires the ability to accurately model forest dynamics under a changing environment, which is difficult using conventional statistical methods as many factors that interactively affect forest growth must be considered. As well, statistical model development is often limited by the lack of broad-scale repeated forest measurements needed to capture changes in 1 or more variables and the corresponding changes in forest dynamics (e.g., growth in diameter and height), while assuming other variables do not change, or their changes do not significantly affect the forest dynamics of interest. In many forested countries, comprehensive monitoring programs have amassed large amounts of diverse forest measurement data. Here we propose a new approach for using artificial neural network-based machine learning to synthesize spatiotemporal tree measurement data collected over a vast area of boreal forest in central Canada to model diameter at breast height (DBH)-height and DBH-height-age relationships for 6 dominant tree species. More than 30 potentially important stand structure, site, and climate variables were considered. We used an individual-based modelling approach by considering each individual tree measurement as an instance of the complex relationships modelled; together, broad-scale long-term monitoring data contain many such instances, representing considerable spatial and temporal scale variation in forest growth and growing conditions. Using this approach, we significantly improved DBH-height and DBH-height-age models. And the models developed allowed us to analyze the effects of environmental conditions or changes in these conditions on forest growth. This may be the first attempt at applying this type of approach, which can be used to more accurately model, for example, forest growth, mortality, and how they are affected by changing climate in a variety of forest types. © 2020 Elsevier B.V.
语种	英语
scopus关键词	Climate models; Dynamics; Machine learning; Neural networks; Complex relationships; Comprehensive monitoring; Diameter-at-breast heights; Environmental conditions; Individual-based modelling; Long-term monitoring datum; Spatio-temporal data; Sustainable forest management; Forestry; age determination; boreal forest; environmental conditions; environmental monitoring; forest dynamics; forest management; forestry modeling; growth; height; machine learning; mortality; spatiotemporal analysis; Data; DBH; Dynamics; Forestry; Growth; Neural Networks; Sustainable Forest Management; Trees; Canada
来源期刊	Forest Ecology and Management
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/155286
作者单位	Ontario Forest Research Institute, Ministry of Natural Resources and Forestry, 1235 Queen Street E., Sault Ste. Marie, ON P6A 2E5, Canada; College of Economics and Management, Nanjing Forestry University, 159 Longpan Road, Nanjing, Jiangsu 210037, China; College of Biology and the Environment, Nanjing Forestry University, 159 Longpan Road, Nanjing, Jiangsu 210037, China; Institute of Environmental Sciences, University of Quebec at Montreal, Case postale 8888, succ Centre-Ville, Montréal, QC H3C 3P8, Canada; Ministry of Natural Resources and Forestry, South PorcupineON P0N 1H0, Canada; Department of Mathematics and Statistics, York University, Toronto, ON, Canada
推荐引用方式 GB/T 7714	Chen J.,Yang H.,Man R.,et al. Using machine learning to synthesize spatiotemporal data for modelling DBH-height and DBH-height-age relationships in boreal forests[J],2020,466.
APA	Chen J..,Yang H..,Man R..,Wang W..,Sharma M..,...&Deng Z..(2020).Using machine learning to synthesize spatiotemporal data for modelling DBH-height and DBH-height-age relationships in boreal forests.Forest Ecology and Management,466.
MLA	Chen J.,et al."Using machine learning to synthesize spatiotemporal data for modelling DBH-height and DBH-height-age relationships in boreal forests".Forest Ecology and Management 466(2020).